We invite you to contribute a book chapter for our Edited Book entitled “Advances in Deep Generative Models for Medical Artificial Intelligence", to be published by Springer Nature publishers. More information available on book webpage https://medicalai.weebly.com/

Submission Guidelines

Step 1:

Submit a 2-page PDF as an extended abstract of the contributed chapter using the easychair link provided below. The extended abstract should highlight the main summary of the contributed chapter and may include headings such as (1) Introduction (2) Methodology (3) Results (4) Summary.

Step 2:

Submit full chapter PDF for review after acceptance of the extended abstract.

For submission, use the easychair link provided below.

The full chapter should be in Springer template.

Each chapter can have 20 – 30 pages in Springer template.

It is the responsibility of the authors to adhere to the guidelines of the publisher.All the chapters must be original contributions of the authors.

In case of using published figures/materials, the authors must obtain prior permission of re-use as required by the publisher.All the chapters will go through similarity software check.

An overall similarity index of less than 15% should be ensured.Reviewing policy: The review process will be single blind.

List of Topics

Potential topics include (but not limited to):

• Generative Adversarial Networks for MRI images.
• Generative Adversarial Networks for medical image data augmentation
• Deep generative models for precision medicine.
• Deep generative models for disease modeling and prognosis.
• Deep generative models for domain-to-domain transformation in medical images.
• Deep learning for ultrasound images
• Deep generative models with 3D architectures for ultrasound sequences.
• Noise adaptation in medical images with deep generative models.
• Vascular ultrasound image analysis using deep generative models.
• Deep generative models for domain adaptation and in-vivo to in-vitro transformation.
• Deep learning for skin cancer diagnosis
• Deep learning on digital mammograms.
• computer-aided diagnosis, image segmentation, tissue recognition, and classification
• Neural Diffusion Models for medical image synthesis.
• Neural Diffusion Models for noise adaptation in medical images.
• Deep generative models on edge computing devices for computer-aided diagnosis.

Editors

• Dr. Hazrat Ali, Hamad Bin Khalifa University, Qatar
• Dr. Mubashar Husain Rehmani, Munster Techological University, Ireland
• Dr. Zubair Shah, Hamad Bin Khalifa University, Qatar

Publication

The book will be published in Springer Series on Studies in Computational Intelligence, by Springer Nature

Contact

All questions about submissions should be emailed to hazrat DOT ali AT live DOT com